Senior GenAI Engineer

Role Overview This role focuses on designing, building, and deploying AI-powered capabilities that serve as shared, reusable infrastructure for the entire bank - AI agent workflows, retrieval-augmented pipelines, enterprise knowledge systems, and integrations with the bank's internal technology ecosystem.The Senior GenAI Engineer works at the intersection of applied AI engineering, enterprise systems integration, and responsible AI delivery - contributing directly to Ameriabank's AI Strategic priorities. Key Responsibilities 1. AI Solution Design & Development Design, develop, and deploy AI-powered automations, agent workflows, and RAG pipelines serving multiple business domains across the bankDesign and evolve the Unified Enterprise Knowledge Base, including document ingestion, structured extraction, entity resolution, ontology/schema design, relationship extraction, evidence provenance, vector indexing, and hybrid GraphRAG retrieval.Develop and maintain reusable AI components, agent templates, and reference architectures that any tribe or squad can build on - embodying the 'build once, enable many' principleDesign and implement Agentic AI frameworks - enabling autonomous multi-step workflows that coordinate across systems, tools, and processes 2. Integration & Enterprise Systems Build and maintain integrations with enterprise systems - Jira, Confluence, TestRail, Azure DevOps - enabling AI-powered business and SDLC automationDevelop internal APIs and microservices connecting AI components with bank infrastructureIntegrate AI capabilities with the n8n (or similar) workflow automation platform - designing and maintaining production-grade AI-powered process automation pipelinesContribute to the SDLC AI Agent System - an end-to-end AI-assisted software development lifecycle covering requirements analysis, test generation, code review, and documentation 3. Research, PoC & Experimentation Lead proof-of-concept development for new AI technologies - multimodal models, voice and speech processing, vision models, and Agentic AI frameworksEvaluate performance, accuracy, cost-efficiency, and security of models both in a controlled on-premise environment and in the cloud.Research and assess emerging open-source models and tooling - recommending adoption decisions based on evidence from structured evaluationContribute to Armenian language model capability - evaluating multilingual models, fine-tuning approaches, and OCR solutions for Armenian document processing 4. Security, Governance & Documentation Ensure all AI solutions adhere to the bank's data classification framework - determining appropriate deployment tier (local on-premise, cloud SaaS, or cloud PaaS) based on data sensitivity Collaborate with Information Security, Cybersecurity, and Operational Risk to ensure secure data handling, audit trails, prompt management, and model transparency Document AI architectures, pipelines, experiments, and design decisions - contributing to the zeusable pattern library and knowledge base of the GenAI Enablement Service Maintain the deployed AI capability registry - ensuring every production system has documented ownership, performance baselines, and governance records Qualifications & Requirements Experience: 5+ years in software engineering or applied AI/ML, with at least 2 years focused on GenAI systems in production environmentsRAG & Knowledge Systems: Proven experience designing and maintaining RAG pipelines and vector databases.Knowledge Graphs: Experience with graph data modeling, entity/relationship extraction, entity resolution, graph traversal and query languages such as Cypher. Experience combining graph and vector retrieval for GraphRAG is highly desirableAgentic Systems: Experience designing tool using LLM applications and multi-step AI workflows, including orchestration, state management, structured outputs, failure handling, human-in-the-loop controls and evaluation. Experience with LangGraph, LangChain, LlamaIndex or similar frameworks is desirablePython & Software Engineering: Strong Python development skills, including testing, packaging, asynchronous programming, API development and production-quality software designAPI & Integration: Solid API development experience and microservice architecture; experience integrating AI systems with enterprise platforms (Jira, Confluence, or similar)Infrastructure: Containerization (Docker), Linux systems, and GPU-based local deployments; on-premise AI environment experience preferredAutomation: Experience with workflow automation platforms - n8n or similar - building production AI-powered process automationSecurity: Understanding of data sensitivity classification, secure AI deployment practices, and compliance requirements in regulated environments Preferred Qualifications & Experience Experience deploying and operating AI in secure, regulated industry environments - banking, financial services, or healthcareData engineering exposure - ETL pipelines, structured and unstructured data processing, SQL and Pandas proficiencyExperience collaborating within Agile or Agile@Scale delivery modelsContributions to open-source AI projects or internal innovation initiativesExposure to banking or financial services domain - understanding of core banking processes, regulatory environment, or financial data